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Estimating maturity from size-at-age data: are real-world fisheries datasets up to the task?

Citation

Wootton, HF and Morrongiello, JR and Audzijonyte, A, Estimating maturity from size-at-age data: are real-world fisheries datasets up to the task?, Reviews in Fish Biology and Fisheries, 30 pp. 681-697. ISSN 0960-3166 (2020) [Refereed Article]


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Copyright Statement

Copyright 2020 Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Reviews in Fish Biology and Fisheries. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11160-020-09617-9

DOI: doi:10.1007/s11160-020-09617-9

Abstract

The size and age at which individuals mature is rapidly changing due to plastic and evolved responses to fisheries harvest and global warming. Understanding the nature of these changes is essential because maturity schedules are critical in determining population demography and ultimately, the economic value and viability of fisheries. Detecting maturity changes is, however, practically difficult and costly. A recently proposed biphasic growth modelling likelihood profiling method offers great potential as it can statistically estimate age-at-maturity from population-level size-at-age data, using the change-point in growth that occurs at maturity. Yet, the performance of the method on typical marine fisheries datasets remains untested. Here, we assessed the suitability of 12 North Sea and Australian speciesí datasets for the likelihood profiling approach. The majority of the fisheries datasets were unsuitable as they had too small sample sizes or too large size-at-age variation. Further, datasets that did satisfy data requirements generally showed no correlation between empirical and model-derived maturity estimates. To understand why the biphasic approach had low performance we explored its sensitivity using simulated datasets. We found that method performance for marine fisheries datasets is likely to be low because of: (1) truncated age structures due to intensive fishing, (2) an under-representation of young individuals in datasets due to common fisheries-sampling protocols, and (3) large intrapopulation variability in growth curves. To improve our ability to detect maturation changes from population level size-at-age data we need to improve data collection protocols for fisheries monitoring.

Item Details

Item Type:Refereed Article
Keywords:fisheries management, maturation, statistical modelling, stock assessment, biphasic growth model, Lester model likelihood profiling, statistical maturity estimates, fisheries-induced evolution, maturity changes, simulations, life history
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Fisheries sciences
Research Field:Fisheries management
Objective Division:Animal Production and Animal Primary Products
Objective Group:Fisheries - wild caught
Objective Field:Fisheries - wild caught not elsewhere classified
UTAS Author:Audzijonyte, A (Dr Asta Audzijonyte)
ID Code:141859
Year Published:2020
Funding Support:Australian Research Council (DP190101627)
Deposited By:Ecology and Biodiversity
Deposited On:2020-11-26
Last Modified:2021-02-22
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